The most common histological type of thyroid cancer is papillary thyroid carcinoma (PTC). Radical resection of the thyroid gland is currently the recommended method of treatment. Almost 75% of thyroidectomies performed just for diagnostic purposes are benign. Thus, the confirmation of innovative and more precise noninvasive biomarkers holds promise for the detection of PTC, which may decrease the number of unnecessary thyroid lobectomies. In this work, using the droplet digital PCR (ddPCR) method, we have analyzed the level of five miRNAs  in the plasma of patients with PTC and compared them with those of a healthy control group to investigate whether miRNAs also have value in the management of PTC. Levels of four miRNAs, namely let-7a, let-7c, let-7d, and let-7f, were significantly higher in PTC patients than healthy controls. Thus, the analysis of circulating let-7 can be a useful tool and support the currently used methods for PTC diagnosis. However, our observation requires further research on a larger patient group. Over the recent decades, there has been an increase in the incidence of thyroid cancer worldwide . PapillaExpression of circulating microRNAs (miRNAs) has been found as potential blood-based biomarker in the diagnosis of cancer . The miRThe differential expression of miRNAs in the circulation of patients with cancer when compared with healthy controls has been documented by numerous studies, making miRNAs a promising noninvasive biomarker. This makes them hopeful diagnostic and therapeutic targets in deregulated pathways in cancer development. Deregulation of the let-7 family is widely explored in many cancer types and is suggested to be a valuable diagnostic tool in liquid biopsy including lung cancer , prostate cancer (let-7a), gastric cancer , breast cancer , ovarian cancer , hepatocellular carcinoma , acute myeloid leukemia , and colorectal cancer (let-7a). However, their potential as prognostic biomarkers has also been emphasized in several cancer types such as myelodysplasia (let-7a), lung cancer , ovarian cancer (let-7f), hepatocellular carcinoma , multiple myeloma (let-7e), and prostate cancer (let-7d) . Let-7 mBefore the practical application, several challenges need to be overcome. Commonly applied relative quantification of circulating miRNAs by quantitative PCR (qPCR) is limited by the difficulty of finding reliable endogenous reference genes in the plasma or serum. Recently, droplet digital PCR (ddPCR) has been shown to overcome these methodical difficulties, allowing absolute quantification without the need for internal/external normalization. Moreover, the ddPCR technique has shown increased precision and sensitivity in detecting low target copies, and it is also relatively insensitive to potential PCR inhibitors, thus producing more reliable results than conventional qPCR .In this study, using ddPCR, we have examined the level of five miRNAs  in the plasma of patients with PTC and compared them with those of a healthy control group to investigate whether miRNAs also have a value in the management of PTC. According to our knowledge, the expression of let-7a, let-7c, let-7d, and let-7f has never been tested in plasma of PTC patients.n = 49) was 48 years, range 20–76. The median age of healthy control individuals (n = 21) was 47 years, range 29–67. The detailed data are presented in We compared the levels of five miRNAs  in plasma, by ddPCR, between the PTC cases and healthy subjects. The median age for cancer patients . The level of let-7i was higher in PTC group compared to control, but the difference is statistically insignificant (Mann–Whitney: p = 0.059)  were significantly higher in PTC patients than healthy controls (Mann–Whitney: = 0.059) .p = 0.424; p = 0.505; p = 0.142; p = 0.689; p = 0.182). Similarly, no statistical differences in miRNA levels were found among women of both groups . Additionally, no statistical change was found in the PTC group itself  as well as in the control group  for all miRNAs  and regional lymph nodes (N) classification was performed according to the system established by the International Union Against Cancer .1 + T2 and T3 + T4 tumor stages . Moreover, no statistical difference was found between the group of patients with regional lymph node metastases (N1) compared to no regional lymph nodes metastases (N0) and the group where lymph nodes could not be assessed (Nx) for all miRNAs   .Receiver operating characteristic (ROC) curve analyses were conducted to calculate the diagnostic value of the selected miRNAs  in the PTC compared to healthy control groups. The areas under the ROC curves (AUCs) were measured to estimate the specificity and sensitivity of miRNA to diagnose patients with PTC. The selection of an optimal cutoff point for discriminating between the PTC group and the healthy control group has been allowed (set) by the Youden index (J). J is the maximum vertical distance between the ROC curve and the diagonal reference line. Youden index is defined as J = maximum (sensitivity) + (specificity) − 1. The data are illustrated in p < 0.05, 95% confidence interval = 0.522–0.796); for let-7c, 50.5 copies/µL with a sensitivity 50.5% and a specificity of 14% ; for let-7d, 6.8 copies/µL with a sensitivity 71% and a specificity of 33% ; for let-7f, 16.2 copies/µL with a sensitivity of 59% and a specificity of 19% ; and for let-7i, this was indicated at 103 copies/µL with a sensitivity of 43% and a specificity of 14% . Combinations of all tested miRNAs have been made, however the results have not been improved. For details, see The levels of let-7a, let-7c, let-7d, and let-7f miRNAs in the PTC cases were significantly higher in comparison with the healthy controls. An optimal cutoff point for let-7a was indicated at 28.5 copies/µL with a sensitivity of 74% and a specificity of 38%  with identical seed sequences. All let-7 miRNAs are produced from 12 genes  located on eight different chromosomes  [The let-7  miRNA was one of the first miRNAs described in the nematode In the present study, we have analyzed five members of the let-7 family. We have noticed significantly increased levels of let-7a, let-7c, let-7d, and let-7f in plasma of PTC patients compared to healthy controls. Noteworthily, to our knowledge, there are currently no studies that have examined expression of these miRNAs in the plasma of PTC patients. Additionally, let-7i showed an increase in expression of PTC plasma samples of borderline significance.The expression of let-7 is different in different cancer types. In breast, colon, prostate and renal cancer, Heneghan et al.  observedThe discrepancy in results may be due to the different nature of various types of cancer. Differences also occur within the same type of cancer and analyzed miRNA, which may result from the use of different methodology and the material used . An example of these are results of Li et al. and Qattan et al. Both authors analyzed let-7c in breast cancer, but Li used serum and Qattan used plasma. Additionally, these highly variable results may be due to a variety of patient variables such as age, cancer stage, and controls used ,20.p = 0.042). Similarly, in our work, for let-7i, the difference between the groups reached borderline significance (p = 0.059).Regarding the analyzed let-7 family members in PTC, let-7a was increased in the tissue compared to control . ContradDespite all let-7 family members having similar structure, let-7i showed the greatest discrepancy in the base sequence against let-7a . This maOn the other hand, a similar structure may indicate a similar function of all let-7 miRNAs, which resulted in the elevated level of all examined let-7 members but at different levels. It has been established on hepatoma cells that when let-7i and let-7g were overexpressed together, it had a greater impact on division and apoptosis than overexpression of separate miRNAs, suggesting that members of this family may act in cooperation . HoweverAbnormal expression of let-7 has been associated with cancer initiation and progression. Substantial evidence suggests that let-7 could function as a tumor suppressor or an oncogene through various stimuli.One of the explanations can be that cancer cells release the intercellular tumor-suppressive miRNAs to the extracellular environment, change tumor microenvironment and support cancer progression. Circulating miRNAs can directly target tissue mRNAs of genes involved in the cell immunity, differentiation, or angiogenesis and consequently may lead to carcinogenesis. Tumor-associated macrophages (TAMs) are a class of immune cells present in high numbers in the microenvironment of solid tumors. Baer et al. demonstrated that increased let-7 expression in TAMs results in the change of M1 into the M2 phenotype. The M2 phenotype is associated with increased angiogenesis and tumor aggressiveness, while TAMs with M1 phenotype show proinflammatory activity and better prognosis . MoreoveOn the other hand, the phenomenon that tumor-suppressing miRNAs were increased reflected the fact that circulating miRNAs were not the main products of cancerous cells but the results of overall immune response and play a significant role in cancer defense and cancer therapy. We have still only begun to understand the complexity of let-7 in immune system regulation. Several reports suggest that let-7 is essential for CD8+ cells maturation, and its expression significantly decreases in late stages. However, the exact mechanism of let-7-mediated regulation is unclear. Pobezinky et al. have shown that the naive CD 8 cells with lin28-mediated knockdown of let-7 expression revealed comprised hemostasis of this cell in vivo, which finally leads to increased proliferation of these cells . This miNext to the diagnostic opportunity, circulating let-7 can be used for therapy monitoring purposes. This requires an individual approach in each type of cancer. Let-7 levels are directly affected by the therapy. For instance, in acute promyelocytic leukemia, let-7c increases rapidly after chemotherapy, then decreases again upon relapse  MoreoverAll of these show the complexity of the connection between let-7 and cancer development. Thus, it is important that let-7 overexpression treatment strategies be personalized concerning individualized clinical approaches based on specific miRNA expression profiles, as opposed to overarching treatment schemes spanning across multiple malignancy types. However, this will require careful consideration and further retrospective trials followed by robust clinical trials .The level of miRNAs in plasma was evaluated in patients admitted to the Department of General and Oncological Surgery of the Medical University of Lodz. The patients were suspected of, or diagnosed with, papillary thyroid cancer based on fine needle aspiration biopsy (FNAB) before the surgery. The surgical procedure was performed on all patients in accordance with the guidelines of the Polish Society of Surgeons and the Polish Society of Oncological Surgery . A totalg, 4 °C) for 10 min. Then, plasma samples were brought to the second centrifugation  to remove the cell debris and were stored at −80 °C for no longer than 4 weeks until miRNAs level measurements were obtained. Total RNA including miRNAs were extracted from 1 mL of plasma using QIAamp ccfDNA/RNA Kit (Cat No./ID: 55184) according to the manufacturer’s instruction. RNA was eluted in 20 μL of nuclease-free water. An equal volume of starting material was used for all the plasma samples to adjust for differences in recovery efficiency between different plasma samples.For total RNA extraction and analysis for miRNAs using ddPCR analysis, 9 mL venous blood was collected from each patient in vacutainer tubes  with EDTA. The samples were collected before surgery and processed within 2 h after venipuncture. Plasma from EDTA blood samples was obtained by centrifugation  . VisiblyThe miRNAs were reverse-transcribed using the TaqMan miRNA Reverse Transcription Kit (#4366596) and the associated miRNA-specific stem-loop primers for five miRNAs: let-7a, let-7c, let-7d, let-7f, and let-7i  with some modifications. A customized RT primer pool was prepared by pooling all miRNA-specific stem-loop primers of interest. Six microliters of this mixture were added to the reaction mix containing 5 μL of extracted total RNA, 0.3 μL 100 mM dNTP, 3 μL enzyme (50 U/μL), 1.5 μL 10× RT buffer, and 0.19 μL RNase inhibitor (20 U/μL). A final volume of 15 μL was reverse-transcribed according to the manufacturer’s instructions. cDNA was then stored at −20 °C. In order to normalized the sample-to-sample variability in miRNA content, a fixed volume of eluted RNA sample was used as input, rather than the same amount of RNA for each sample. Indeed, the yield of RNA from a small volume of plasma/serum has been reported to be below the limit of accurate quantitation by spectrophotometry, thus miRNA contents extracted from plasma/serum has been reported to be undetectable by using NanoDrop spectrophotometer.2O. Droplets were generated by loading the mixture into a single-use cartridge with 70 μL of QX100 Droplet Generation oil (Bio-Rad Laboratories). Cartridges were then placed into the QX200 Droplet Generator (Bio-Rad Laboratories). The droplets generated from each sample were transferred to a 96-well PCR plate , and PCR amplification was carried out on the C1000 Touch Thermal Cycler (Bio-Rad Laboratories), according to the manufacturer’s protocol. The thermal cycling conditions were as follows: 95 °C for 10 min, 40 cycles of 95 °C for 15 s and 60 °C for 1 min, and a final inactivation step at 98 °C for 10 min. NTCs were included in every PCR plate. The plate was then loaded on the QX200 Droplet Reader (Bio-Rad Laboratories) and read automatically. The fraction of PCR-positive droplets was quantified assuming a Poisson distribution [Total plasma levels of let-7a, let-7c, let-7d, let-7f, and let-7i were quantified using the ddPCR system (Bio-Rad Laboratories) in Central Scientific Laboratory “CoreLab” of the Medical University of Lodz. Briefly, 1 μL of the synthesized cDNA was added to a 19 μL PCR reaction mixture containing 10 μL of digital PCR ™ supermix (Bio-Rad Laboratories), 1 μL of TaqMan primer/probe mix (Applied BioSystems) and RNase-free Hribution . In detap-values ≤ 0.05 were considered statistically significant. Numerical data were expressed as mean ± SD (median).All statistical analyses were performed using Dell Statistica , version 13 (software.dell.com). The Shapiro–Wilk test showed that our data were not normally distributed. The nonparametric Mann–Whitney test was used for comparison between two groups. The Kruskal–Wallis test (nonparametric ANOVA) was used for comparison between the three groups. For the prediction of cutoff values of the markers studied, the receiver operating characteristic (ROC) curve have been used. All


Enterovirus 71 (EV71) infection is an endemic disease in Southeast Asia and China. We have previously shown that EV71 virus causes functional changes in mitochondria. It is speculative whether EV71 virus alters the host cell metabolism to its own benefit. Using a metabolomics approach, we demonstrate that EV71-infected Vero cells had significant changes in metabolism. Glutathione and its related metabolites, and several amino acids, such as glutamate and aspartate, changed significantly with the infectious dose of virus. Other pathways, including glycolysis and tricarboxylic acid cycle, were also altered. A change in glutamine/glutamate metabolism is critical to the viral infection. The presence of glutamine in culture medium was associated with an increase in viral replication. Dimethyl α-ketoglutarate treatment partially mimicked the effect of glutamine supplementation. In addition, the immunoblot analysis revealed that the expression of glutamate dehydrogenase (GDH) and trifunctional carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD) increased during infection. Knockdown of expression of glutaminase (GLS), GDH and CAD drastically reduced the cytopathic effect (CPE) and viral replication. Furthermore, we found that CAD bound VP1 to promote the de novo pyrimidine synthesis. Our findings suggest that virus may induce metabolic reprogramming of host cells to promote its replication through interactions between viral and host cell proteins. Picornaviridae + ion  for positive ion mode or [M-H]- ion  for negative ion mode, was used as the lock mass at a concentration of 200 ng/mL and a flow rate of 10 μL/min. Liquid chromatography-time-of-flight-mass spectrometry (LC-TOF-MS) was performed, as previously described . Liquid + .All spectral data were processed for peak picking, alignment and normalization, using Progenesis QI data analysis software . The peak intensities of all features were obtained. The features were identified through search in Metabolite Link (METLIN)  and Humam/z. The trap collision energy was set from 5 to 35 V. For validation of the metabolites, the standard compounds were subjected to chromatographic separation, and MS and MS/MS under the condition identical to that of metabolite profiling. MS/MS spectra were collected at a speed of 0.3 s per scan, with a medium isolation window of 4 g for 10 min at 4 °C. The cell pellet was lysed in lysis buffer  supplemented with cOmplete Mini EDTA-free protease inhibitor cocktail , and was subjected to three freeze-thaw cycles. The Dynabead Protein G magnetic beads  were processed according to the manufacturer’s instruction, and were bound to 2 μg of c-Myc monoclonal antibody (9E10)  for 10 min at room temperature. After antibody binding, the beads were washed gently with PBS (pH 7.4)/0.02% Tween 20. The magnetic beads were separated with a magnet, and after removal of the wash buffer, the antibody-coated beads were incubated with 500 μg of cell lysate overnight at 4 °C. The protein-bound beads were washed three times with wash buffer and two more times with PBS. The protein was eluted from beads thrice with 50 μL 0.1% trifluoroacetic acid/50% acetonitrile, and the eluates were combined and lyophilized using centrifugal evaporator. The sample was dissolved in 50 mM NaHCO3 (pH 8.0); treated with 10 mM dithiothreitol at 57 °C for 1 h; and alkylated with 10 mM iodoacetamide at room temperature for 30 min. The sample was then digested with trypsin  at an enzyme/substrate ratio of 1:20 at 37 °C overnight. The digested samples from EV71-infected and control cells were labeled with heavy (13CD2O) and light (12CH2O) formaldehyde in the presence of sodium cyanoborohydride. The reaction was allowed to proceed for 1 h at 37 °C and quenched by the addition of ammonium bicarbonate . The dimethylated peptides were mixed and dried. The dried sample was dissolved in 0.1% formic acid and desalted using RP micro-column . The sample was subjected to Ultimate 3000 two-dimensional strong cation exchange (SCX)/reverse-phase nanoscale liquid chromatography (LC) coupled with Orbitrap Elite hybrid mass spectrometer . The peptides were fractionated on a SCX column , using an ammonium chloride gradient with 30% acetonitrile, into six fractions over 540 min. During separation, each fraction was diluted online with 0.1 % formic acid, trapped with a RP trapping column, and then separated on a RP column  using an acetonitrile gradient in 0.1% formic acid over 90 minutes. The mass spectrometer was operated in positive ion mode, and the full scan spectra (m/z 400–2000) were acquired by the orbitrap analyzer, with the resolution set at 60,000. MS/MS spectra were acquired in a data-dependent manner in the ion trap. From each MS spectrum, the top 15 most intense precursor ions with an intensity greater than 5000 were selected for MS/MS fragmentation. Each precursor was sequenced twice and then excluded for 75 s. The MS data were processed using the Proteome Discoverer software (version 1.3.0.339) for protein identification and quantification. The MS/MS spectra were searched against the Swiss-Prot human database  using the Mascot search engine . The parameters for database search were set as below: enzyme specificity, trypsin; maximum missed cleavage, 2; fixed modification, carbamidomethylation of cysteine; variable modifications, acetylation of protein N-terminal amino acids, oxidation of methionine, and glutamine to pyroglutamic acid conversion at peptide N-terminus, dimethylation of light and heavy version labels at lysine and peptide N-terminus; mass tolerance, 10 ppm and 0.6 Da for MS and MS/MS spectra, respectively. The false discovery rate of the search result was estimated by searching against a decoy database, and was set to 0.01 for both peptide and protein levels. Finally, proteins identified with at least two unique peptides were considered as confident identifications, and quantification was done by calculating the median of ratios of the corresponding unique peptides of each protein. Any proteins that were differentially co-immunoprecipitated with GFP-VP1-Myc versus with GFP-Myc represent those cellular proteins interacting with VP1. Vero cells were transfected with expression vectors encoding the GFP-VP1-Myc and GFP-Myc (c-Myc-tagged GFP as control), as described above. The culture medium of the transfected cells was removed. After two washes in PBS, the cells were scraped off the culture plate, collected in a microfuge tube, and centrifuged at 16,000 × For studying the interaction between VP1 and CAD, we transfected Vero cells with expression vectors encoding GFP-VP1-Myc and N-FLAG-CAD (or C-FLAG-CAD). Immunoprecipitation was performed using Dynabead Protein G magnetic beads coated with 2 μg of c-Myc monoclonal antibody (9E10) or FLAG M2 monoclonal antibody  as described in the preceding section. For western blotting of cell lysate, the cells were lysed in the same lysis buffer as that used in immunoprecipitation. For the analysis of the immunoprecipitation, the protein-bound beads were heated in Laemmli sample buffer at 95 °C for 5 min. The sample was analyzed by western blotting, as previously described . The ant6 cells were seeded in a T75 culture flask, and mock- or transfected with the indicated vectors 24 h later. After 20 h, the culture medium was replaced with MEM supplemented with 40 mM NaH13CO3, 10 mM HEPES and 10% FCS. For control, the medium contained the unlabeled NaHCO3. The cells were incubated at 37 °C in an incubator without CO2 supply for 2 h, and then treated with 20 μM brequinar sodium , dihydroorotate dehydrogenase (DHODH) inhibitor, for 1 h. The cells were extracted, as described in 13C]-dihydroorotate (m/z 158.0283) was measured via a targeted metabolomic approach. The level of [13C]-dihydroorotate accumulating in the presence of BRQ was divided by the labeling time, and it indicates the average flux of CAD-catalyzed reactions. For the experiment involving the EV71 infection, about 1.2 × 106 cells were seeded in a T75 culture flask, and infected with virus at the indicated MOI for 1 h. The culture medium was replaced with MEM supplemented with 40 mM NaH13CO3, 10 mM HEPES and 1% FCS. For control, the medium contained the unlabeled NaHCO3. The cells were incubated at 37 °C in an incubator without CO2 supply for 2 h, and then treated with 20 μM BRQ for 1 h. The [13C]-dihydroorotate was quantified, as described above. About 1 × 10The statistical analyses of metabolomic and proteomic data are described in the respective sections. The statistical analyses of other experimental data were performed with GraphPad Prism 5 software . Two-way analysis of variance (ANOVA) with Sidak’s multiple-comparison test, two-tailed unpaired Student’s t test, a Kruskal-Wallis test with Dunn’s multiple comparison test, and a Mann-Whitney test were used where appropriate. To study the metabolic changes associated with EV71 infection, we infected Vero cells with EV71 at an MOI of 0, 0.3125, 0.625, or 1.25, and extracted their metabolites for metabolomic analysis. The cell extracts were analyzed by UPLC-Q-TOF-MS operated in electrospray ionization (ESI) positive and negative modes. The datasets corresponding to mock-infected cells and those infected at different MOIs were analyzed by partial least squares discriminant analysis (OPLS-DA). The data points corresponding to cells infected at different MOIs were spatially separated from one another and from mock-infected cells, indicating an MOI-dependent change in metabolism of infected cells A. FeaturA number of amino acids and dipeptides changed in abundance in cells after infection. As shown in The metabolites of interest were mapped onto metabolic pathways through the use of algorithms available from KEGG and HMDB databases. A number of metabolites related to glutathione, glycolytic intermediates, citric acid cycle intermediates, and purine and pyrimidine nucleosides, changed significantly .Several γ-glutamyl dipeptides, which are most likely involved in amino acid transport and glutathione metabolism , appeareEV71 infection appears to be associated with oxidative stress. The levels of glutathione (GSH) and glutathione disulfide (GSSG) increased with MOI. However, the latter was elevated to a greater extent than the former. The GSSG/GSH ratios of cells infected at MOIs of 0.625 and 1.25 were 5.16 and 14.67, respectively. There were changes in precursors for GSH synthesis in these cells. The levels of cysteine and γ-glutamyl cysteine increased modestly at the MOI of 0.3125 and declined at higher MOIs. Glycine level decreased significantly as MOI increased. These findings suggest that the steady state level of GSH may increase at the expense of its precursors. The abundance of fructose bisphosphate (FBP), the product of rate-determining step of glycolysis, increased 18-29-fold in the infected cells, depending on MOI. The levels of 3-phosphoglycerate (3PG) and phosphoenolpyruvate (PEP) increased non-significantly at low MOIs. In contrast, the downstream products pyruvate decreased in abundance in the infected cells. These findings suggest that the glycolysis may be enhanced in the infected cells to produce pyruvate, which can be converted to other molecules, such as citrate or oxaloacetate. There were concomitant changes in the intermediates of citric acid cycle. Citrate levels dropped by 45% to 60% in cells infected at MOI ranging from 0.3125 to 1.25. Apparently, the pools of α-ketoglutarate and succinate did not change significantly upon infection. In contrast, the amount of fumarate, malate, and oxaloacetate declined in the infected cells, with the extent of reduction in the former two metabolites being lower than that of oxaloacetate. The levels of fumarate and malate were respectively 17.2% and 8.7% lower in cells infected at an MOI of 0.625, as compared to the mock-infected cells. The oxaloacetate level was lowered by 81.6% in these cells, as compared to that of control. These findings imply that glutamate may feed into citric acid cycle at the point of α-ketoglutarate to maintain the pools of α-ketoglutarate and succinate, and that their downstream products fumarate, malate and oxaloacetate are most likely consumed through reactions other than citrate formation.The levels of uridine monophosphate (UMP), adenosine monophosphate (AMP) and guanosine monophosphate (GMP) increased in infected cells. The levels of glycine and aspartate, which are required for purine and pyrimidine biosynthesis, decreased substantially in the infected cells. Another precursor glutamine, which was supplied in culture medium, did not change significantly in the infected cells, with the exception that it was slightly reduced at the highest MOI used. However, glutamate, the by-product of the glutamine-dependent reactions in purine and pyrimidine biosynthesis, increased significantly in abundance in the infected cells. These findings suggest that the purine and pyrimidine biosynthesis may be up-regulated in the infected cells.As stated earlier, the significant increase in glutamate in infected host cells is indicative of continual uptake of glutamine and its utilization. It is hypothesized that glutamine is essential to viral replication. To test this possibility, we varied the concentration of glutamine in culture medium, and examined its effect on the viability of infected cells. Despite an effect of glutamine on the growth of uninfected cells , its preIt is evident that glutamine acts to enhance viral replication through αKG-dependent and independent pathways. To study the exact metabolic pathways involved, we examined the temporal changes in the expression of glutamine metabolism-related enzymes, including glutaminase (GLS), GDH and CAD, in host cells during infection. GLS, in concert with GDH, converts glutamine to αKG; and CAD utilizes glutamine for production of dihydroorotate, a precursor for pyrimidine biosynthesis. As shown in To test if these glutamine metabolism-related enzymes play important roles in viral replication, we transfected Vero cells with siRNA against GLS, GDH and CAD; infected them with EV71; and evaluated the effect of knockdown on the host cell viability and viral replication. As shown in To study the viral proteins that bind to host proteins to regulate metabolism, we employed a proteomic approach to screen for those cellular proteins interacting with viral proteins. We constructed a series of vectors encoding the c-Myc-tagged fusion proteins of green fluorescent protein (GFP) and EV71 proteins. The immunoprecipitation was performed with anti-c-Myc antibody ), and any co-immunoprecipitated proteins were subjected to proteomic analysis. One of these co-immunoprecipitated proteins was identified as CAD. To validate such interaction, we transfected Vero cells with expression vectors encoding GFP-VP1-Myc and FLAG-CAD (either N-FLAG-CAD or C-FLAG-CAD), and performed immunoprecipitation with c-Myc monoclonal antibody, or reciprocally with FLAG M2 antibody. The immunoprecipitated proteins were detected with anti-c-Myc and anti-FLAG antibodies. As shown in We sought to examine whether VP1 interacts with endogenous CAD. Vero cells were transfected with expression vectors encoding GFP-VP1-Myc and GFP-Myc, and the cell lysate was immunoprecipitated with anti-c-Myc antibody. The immunoprecipitate was analyzed for the presence of endogenous CAD. As shown in 13CO3 in the presence of BRQ, a potent and selective DHODH inhibitor. In this manner, the newly synthesized [13C]-dihydroorotate accumulated in cells, and the rate of its accumulation is indicative of the average flux of CAD-catalyzed reactions. To study if VP1 increases such flux, we transfected Vero cells with the expression vector of VP1, labeled the transfected cells with NaH13CO3, and subsequently treated with BRQ. The labeled cells were extracted for determination of [13C]-dihydroorotate. As shown in 13CO3 in the presence of BRQ. The labeled cells were extracted and analyzed accordingly. Infection increased the relative flux of CAD-catalyzed reactions in an MOI-dependent manner -, hepatitis C virus (HCV)-, and Kaposi’s sarcoma-associated herpesvirus (KSHV)-infected cells ,42,43. TEnteroviral infection induces mitochondrial generation of reactive oxidative stress . ConsistThere exist specific changes in amino acid pools in the EV71-infected cells. Several amino acids, including glutamine, glycine and aspartate, are involved in purine and pyrimidine biosynthesis. Glycine that is consumed in biosynthesis of purine and GSH decreased in abundance. The level of aspartate that is used for purine and pyrimidine biosynthesis decreased in a similar manner. In contrast, the intracellular level of glutamine whose concentration in medium is 2 mM was largely maintained in the infected cells. The glutamine level decreased modestly only in the cells infected at high MOI. As mentioned in the preceding section, glutamate may arise from GLS-catalyzed reaction. Other pathways, such as histidine catabolism, may also contribute to intracellular glutamate pool. Apart from the dehydrogenation reaction, glutamate may participate in transamination of oxaloacetate and pyruvate to aspartate and alanine, respectively. Consistent with our findings, changes in amino acid pools have been found in echovirus 30-infected cells . InteresThere are specific changes in γ-glutamyl dipeptides in the infected cells. The γ-Glu-Leu level was reduced in an MOI-dependent manner, whereas the γ-Glu-Ala level showed an opposite trend . The γ-gIn the present study, a host cell model in which virus replicates efficiently was employed. Vero cells are epithelial cells derived from African green monkey, and are able to support robust replication of EV71. For other cell types known to be infected by EV71 ,58, studHost cells obtain pyrimidine nucleotides via de novo biosynthetic and salvage pathways . During Apart from its role in necleocapsidation, viral capsid proteins have other functions in viral replication. Hepatitis C virus (HCV) core protein binds to RNA-dependent RNA polymerase to inhibit its RNA synthetic activity . Brome m


To assess the validity and reliability of the Japanese version of the Daily Record of Severity of Problems  for evaluating symptoms of premenstrual syndrome (PMS), and to develop a short form version of the J-DRSP.Using the “DRSP-JAPAN” smartphone app, we collected daily J-DRSP records from cycle day − 6 (CD − 6) to CD 10, with CD 1 representing the menstruation onset date. Factorial validity  and criterion validity were examined, and test-retest reliability (intraclass correlation: ICC) evaluated. The short-form version of the J-DRSP was developed using classical test theory.P < 0.001). However, the model fitness of CFA was found to be suboptimal (comparative fit index (CFI): 0.83, root mean square error of approximation (RMSEA): 0.12). Total scores for J-DRSP and the sum scores for each subscale were higher on CD 0 than on CD 10 (p < 0.001), suggesting validity for some criteria. ICC values for the total J-DRSP score from CD 0 to CD − 1, and between CD 9 to CD 10, were 0.60 (95% CI: 0.48–0.72) and 0.76 (95% CI: 0.69–0.82), respectively. Having eliminated some original items after considering factor loading for each item, we developed an 8-item Short-Form J-DRSP (J-DRSP (SF)) comprising 2 factors . CFA showed a better model fit , and ICC values in the luteal and follicular phases were 0.61 (95%CI: 0.51–0.68) and 0.70 (95%CI: 0.62–0.77), respectively.In total, 304 women participated and 243 recorded symptoms on at least 4 days spanning the week of the luteal phase (CD − 6 to CD 0) and 4 days spanning the week of the follicular phase (CD 4 to CD 10), with CD 0 set as the day before menstruation started. The EFA revealed a two-factor structure. Kaiser-Meyer-Olkin was 0.992, and Bartlett’s test of sphericity chi-square was 3653.89  has a two-factor structure and can be used effectively among Japanese women to assess their PMS symptoms.The online version contains supplementary material available at 10.1186/s13030-021-00208-z. Premenstrual syndrome (PMS) is the emotional and physical condition that appears in the luteal phase and resolves after menstruation starts. It is estimated that roughly 30% of women of reproductive age suffer from moderate to severe PMS . If idenTo accurately diagnose PMS, prospective daily recording for at least two consecutive menstrual cycles is critical, as it allows for the relationship between cycles and symptoms to be determined . Of the The Japanese version of the DRSP (J-DRSP) was developed in 2020 in accordance with the necessary translation and cultural adaptation guidelines published by the Patient-Reported Outcomes (PRO) Consortium , 8. ThisThe next necessary step in this process is to determine the factorial validity and test-retest reliability of the J-DRSP. In addition, development of a short-form of the J-DRSP would be ideal, as our previous study determined that the median time to complete the J-DRSP was 2 min, and both women with almost no premenstrual symptoms and those with very severe premenstrual symptoms complained that the use of the J-DRSP on a daily basis is cumbersome .This study aimed to assess the validity and reliability of J-DRSP, and to develop a short-form version of the J-DRSP.The original DRSP contains 21 items pertaining to premenstrual symptoms and 3 items to describe dysfunction in daily life caused by these symptoms, and is based on diagnostic criteria for PMDD from the DSM-IV , participants were prompted to log the start date of menstruation. Participant J-DRSP data are logged daily until the participants are notified that the research study has come to an end. Data from each cycle comprise those from half of the 7 days before menstruation starts and half of the days spanning day 4 to day 10 after menstruation begins. Once those data are collected, no further data are collected for that cycle.In order to assess factorial validity, we conducted exploratory factor analysis (EFA) using data for the 21 symptom items on the day before menstruation began. To measure the factorability of the correlation matrix, we conducted the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity. Eigenvalues and screen plots were used to determine the number of factors, and promax rotation was used. Next, confirmatory factor analysis (CFA) was performed to assess the adoption. We then calculated the comparative fit index (CFI) and root mean square error of approximation (RMSEA) in order to analyze model fitness. CFI values range from 0 to 1, with larger values indicating a better fit. A CFI value of 0.95 or higher is regarded as a good fit . RMSEA rP < 0.05 was considered statistically significant.To confirm criterion validity, we compared the J-DRSP total score obtained the day before menstruation began to that obtained 10 days after the menstruation start date using the Wilcoxon signed-rank test. To examine test-retest reliability, J-DRSP scores from each of the 2 days of the follicular phase and the luteal phase were compared. Specifically, with cycle day 1 (CD 1) and CD 0 set to represent the menstruation onset date and the day before the menstruation onset date, respectively, we compared data from CD 9 to CD 10 and CD − 1 to CD 0.We determined the intraclass correlation coefficient (ICC), percent agreement, and weighted kappa coefficient. Percent agreement and kappa coefficients were calculated after the total J-DRSP was categorized into six levels. As a sensitivity analysis, ICC was determined for any participant whose symptoms interfered with daily life. Those were selected using J-DRSP items 22, 23, and 24, which concern dysfunction in productivity, social activities, and relationships by the symptom items from item 1 to 21, and those who scored 1 (none) for all of items 22, 23, and 24 were excluded. To measure internal consistency, we also calculated Cronbach’s alphas for the total J-DRSP score as well as for each subscale.The short-form J-DRSP (J-DRSP (SF)) was created using classical test theory. First, items for which the factor load was less than 0.5 were excluded after conducting EFA for the original J-DRSP with promax rotation. Next, any items demonstrating sufficient model fit were explored using RMSEA and CFI. After developing the J-DRSP (SF), we conducted CFA and determined Cronbach’s alphas, ICCs, and criterion validity for the J-DRSP (SF) total score and each subscale. ICC values were calculated in both the follicular and luteal phases, i.e., CD 9 to CD 10 and CD-1 to CD 0, respectively. Sensitivity analysis was conducted again using J-DRSP items 22, 23, and 24. Stata 15.3 was used for all statistical analyses.The Kyoto University Ethics Committee approved the study protocol (R1593). Written informed consent was obtained from each participant.In total, 304 women  gave their informed consent to participate in the study and successfully downloaded the app. There were 184 participants in their 20s, 67 in their 30s, 44 in their 40s, and 9 in their 50s.The median number of recorded days was 33 . Of these, 243 logged their symptoms on at least 4 days of the week spanning the luteal phase (CD − 6 to CD 0) and 4 days of the week spanning the follicular phase (CD 4 to CD 10), with CD 1 set as the menstruation onset date. Data from these participants were analyzed.P < 0.001), implying an adequate EFA with a sufficient sample size. Factor 1 and Factor 2 were labeled as the Psychological factor and Physical factor, respectively. The EFA coefficients for each item are shown in Table Data from the day before menstruation started (CD 0) were collected from 228 participants who logged symptoms on that day; these were used in the EFA. Eigenvalues and the scree plot suggested a two-factorial component, with a KMO of 0.992 and Bartlett’s test of sphericity chi-square of 3653.89 (p < 0.001), and sum scores for each subscale were as well . ICCs for the total J-DRSP score for the period of CD 0 to CD − 1, and CD 9 to CD 10, were 0.60 (95% CI: 0.48–0.72) and 0.76 (95% CI: 0.69–0.82), respectively  than on CD 10   was examined to ensure sufficient model fit. The final model was created and CFA was conducted .Cronbach’s alphas for the J-DRSP (SF), S-Psych, and S-Phys were 0.89, 0.89, and 0.80, respectively. ICCs for the J-DRSP (SF), S-Psych, and S-Phys in the luteal and follicular phases were 0.61 (95%CI: 0.51–0.68) and 0.70 (95%CI: 0.62–0.77), respectively Fig. . SensitiIn this study, daily J-DRSP records were collected from Japanese women using the DRSP-JAPAN smartphone app. According to the app’s function, there were no missing data when the participants recorded at least one item, the rate of DRSP completer was higher than the previous study and data management became easier . AlthougThe original DRSP has 3 clinically determined subscales that comprise 6 items for Depressive Symptoms, 4 items for Physical Symptoms, and 2 items for Anger/Irritability . There aThe newly developed J-DRSP (SF) contains 8 items in total . The CFA of the J-DRSP (SF) showed a better model fit than that of the original J-DRSP, suggesting higher validity as a scale to assess PMS symptoms among Japanese women. ICC and Cronbach’s alpha also indicated sufficient reliability of the J-DRSP (SF).In clinical settings, J-DRSP (SF) in the luteal phase could be used as a self-awareness checklist for women who may not yet realize that their symptoms are related to PMS. In addition, checking the J-DRSP (SF) could help identify whether their main problems are more physical or psychological in nature, which could help with effective clinical management of PMS.This study has some limitations. First, none of our participants were patients, and those with and without subjective PMS symptoms were included in the analysis. The original DRSP was developed as a PMDD diagnostic tool and validated as a PMDD scale, although it is widely used for PMS. Floor effects can explain the lower ICC in this study relative to results from the original DRSP . Second,Finally, diagnostic properties of the J-DRSP (SF) were not analyzed in this study. The original DRSP is used as a diagnostic tool to clarify the relationship between PMS-like symptoms and the menstrual cycle. Future studies should compare J-DRSP (SF) to the DRSP as a diagnostic tool.The J-DRSP exhibited moderate to good reliability and certain level of validity. The 8-item J-DRSP (SF), with its two-factorial structure, can be used as an optimized form of the J-DRSP to evaluate PMS symptoms among Japanese women.Additional file 1: Supplementary Figure 1. Screenshots of the DRSP-JAPAN app.


Health emergencies, such as epidemics, have detrimental and long-lasting consequences on people’s mental health, which are higher during the implementation of strict lockdown measures. Despite several recent psychological researches on the coronavirus disease 2019 (COVID-19) pandemic highlighting that young adults represent a high risk category, no studies specifically focused on young adults’ mental health status have been carried out yet. This study aimed to assess and monitor Italian young adults’ mental health status during the first 4 weeks of lockdown through the use of a longitudinal panel design.n = 97) provided self-reports in four time intervals  in 1 month. The Syndromic Scales of Adult Self-Report 18-59 were used to assess the internalizing problems , externalizing problems , and personal strengths. To determine the time-varying effects of prolonged quarantine, a growth curve modeling will be performed.Participants (The results showed an increase in anxiety/depression, withdrawal, somatic complaints, aggressive behavior, rule-breaking behavior, and internalizing and externalizing problems and a decrease in intrusive behavior and personal strengths from T1 to T4.The results contributed to the ongoing debate concerning the psychological impact of the COVID-19 emergency, helping to plan and develop efficient intervention projects able to take care of young adults’ mental health in the long term. The novel coronavirus disease 2019 (COVID-19) is a highly infectious disease that began as a viral pneumonia in late December 2019. In March 2020, the World Health Organization (WHO) declared the state of pandemic.As rapidly pointed out , the COVStarting with China and followed by other states, extraordinary measures and containment efforts  aimed to prevent the high risk of contagion and limit the COVID-19 outbreak have been adopted. In Europe, Italy was the first country that had to face the pandemic. Here, on March 09, 2020, strict lockdown measures were imposed by the government. A series of decrees imposed restrictions on the movements of individuals in the entire national territory from March 10 until May 3. During the lockdown, people were allowed to leave their homes only for limited and documented purposes. Schools, universities, theaters, and cinemas, as well as any shops selling non-essential goods were, therefore, temporarily closed.As previous studies demonstrated , health Within the stream of research investigating the impact of quarantine during epidemics on individual’s mental health, there have been very few longitudinal investigations aimed at understanding and monitoring the changes in the mental health status during quarantine . Where lRecent psychological research on COVID-19 has also highlighted that specific target groups are more at risk than others to develop a wide variety of psychological problems, such as medical workers, marginalized people , and young adults. Regarding young adults (18–30 years old), recent researches have highlighted that they present higher levels of anxiety, distress, and depression than do other adult groups . These fTo fill this gap, the current study presents a longitudinal panel design aimed to assess the Italian young adults’ mental health status and monitor their mental health trends during the firsts 4 weeks of lockdown imposed from the Italian government during the COVID-19 outbreak. On the basis of recent literature on the general population, an increase in mental health problems among young adults during quarantine was hypothesized.Participants were enrolled online and provided self-reports over 1 month . Participants were considered eligible for participation if they met the following inclusion criteria: (a) were between 19 and 29 years and (b) were in a lockdown condition. Exclusion criteria were as follows: (a) diagnosis of psychiatric disorder and/or psychopharmacological treatment (assessed with filter questions in the survey) and (b) not “absolute” lockdown condition .N = 120), nine participants did not participate at T2 (N = 111); four other participants did not participate at T3 (N = 107); and 10 other participants did not participate at T4. These participants were, therefore, excluded from the data analysis. The final simple-size was composed of 97 participants.From the initial sample size of T1  in 1 month. The first survey (T1) was made at the end of the first week of lockdown. The second survey (T2) coincided with the end of the second week of the lockdown. The third survey (T3) coincided with the end of the third week of the lockdown. The fourth survey (T4) coincided with the end of the fourth week of the lockdown.Participants were informed about a complete guarantee of confidentiality and the voluntary nature of participation and their right to discontinue at any point. The enrollment procedure was carried out through an online advertising on social platforms. Participants voluntarily accessed the online platform used for data collection once a week for the 4 weeks of administration. To ensure anonymity, a request was made to create a personal identification code to be used for the four administrations.The Syndromic Scales of Adult Self-Report 18-59  were useThe ASR is especially valuable when used routinely, as in this study design. The ASR norms provide a standardized benchmark with which to compare what is reported by each individual. Standardized reassessments over a regular interval enable to identify reported stabilities and changes in a group who have particular kinds of problems. In this case, the ASR instrument was administered at regular intervals of 1 week for 4 weeks in the period of the Italian lockdown. The ASR was developed both to document specific problems and to identify syndromes of co-occurring problems. In this study, six specific Syndromic Scales, Anxious/Depressed, Withdrawn, Somatic Complaints, Aggressive Behavior, Rule-Breaking Behavior, and Intrusive were used. Anxious/Depressed (18 items) refers to anxiety and depressive symptoms . Withdrawn (8 items) mainly refers to attitudes of isolation and lack of contact with others . Somatic Complaints (12 items) include physical illness, without a known medical cause . Aggressive Behavior (15 items) includes behaviors and attitudes characterized by poor control of one’s aggression . Rule-Breaking Behavior (14 items) refers to transgressive behavior and violation of social norms . Intrusive (6 items) refers to the difficulty faced in the interpersonal relationships and to the prevalence of intrusive behavior . In addition, the broadband scales, Internalizing and Externalizing, were computed. Internalizing problems reflect internal distress, while externalizing problems reflect conflicts with other people. The Internalizing scale consists of the syndrome scales Anxious/Depressed, Withdrawn, and Somatic Complaints, whereas the Externalizing scale consists of Aggressive Behavior and Rule-Breaking Behavior. Moreover, the scale of Personal Strengths (11 items) was used to assess the adaptive functioning of the individuals .not true), 1 (somewhat or sometimes true), and 2 (very true or often true); and a total score may be calculated. Higher raw scores indicate more problematic behaviors on each scale. Then, a normalized T score—weighted for sex and age—was assigned for the Syndromic Scales and to each Internalizing and Externalizing Problem scales. Raw scores of the both types of scales have been quantitatively converted in terms of gender- and age-specific T scores. Clinical significant threshold is indicated by T-scores ≥ 70. Borderline range is from 65 to 69.The items are scored on a three-point rating scale: 0  and McDonald’s omega for categorical data (ω).g; First, the mean differences between the four time intervals  were performed. The unbiased sample estimate of standardized mean difference effect sizes  models were used to estimate the growth trajectories  of the Syndromic Scales of the ASR—both Internalizing and Externalizing scales—and the personal strength scale. Models also estimated subject variability in change across time, as represented in random-intercepts coefficients. Parameters in each GCA model were computed with maximum likelihood (ML) estimation.First, a null model was estimated to provide a baseline comparison and to calculate the intraclass correlation coefficient (Model 0—Intercept only). Second, a null model with covariates was specified (Model 1—Intercept model with covariates). Third, a linear model with time as predictor and covariate interactions was estimated (Model 2—Linear model with covariates). Fourth, a quadratic model was specified with linear interaction effects of the covariates (Model 3—Quadratic model with linear covariates interactions). Fifth, a quadratic model was specified with all possible interactions of the covariates (Model 4—full quadratic model with covariates). Equations of each model are reported in Several models were estimated for each of the outcome variables, separately. Specifically, it was hypothesized that the time (the week of quarantine) could have had an effect on the ASR Syndromic Scales. In addition, it was also hypothesized that covariates, such as sex and the experience of COVID-19 (EXP-CVD19), intended as the experience of direct proximity with relatives and/or friends affected by COVID-19, could have had an effect on the shape of the growth curve across time. Models were sequentially specified according to the guidelines . First, Wh): given a set of competing models and the unknowable true model, the Wh indicates the probability that a model h is the best approximate model (the model with the large Wh is the best-fitting model) ; (III) the evidence ratio (Eh) that expresses the difference—in odds—between the best-fitting model and the first worst-fitting model: the higher the Eh, the more plausible is the best-fitting model.The best model fit was assessed with several indices. First of all, the likelihood ratio test (LRT) was performed between one model and the following one in a step-up approach analysis: Model 0 vs. Model 1; Model 1 vs. Model 2; Model 2 vs. Model 3; and Model 3 vs. Model 4—the most parsimonious model will be preferred . In addiSD = 2.88; range = 19–29). A total of 29 participants (29.9%) had experienced proximity with a COVID-19-infected relative or friend. Most of the participants lived with their parents during the quarantine (80.4%). All participants came from the Campania region, in Southern Italy, and attended the university.Of 97 participants, 48 were male (49.5%) and 49 were female (50.5%). The mean age of the sample was 24.62 (g) are displayed in Means and standard deviations between the four time intervals  and the effect size of means difference  revealed that the variance related to the random intercept of the participants was equal to 24.41.b = −0.961, SE = 3.460, t = 0.077, p = 0.782) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  or the two simple main effects . However, the model revealed a statistically significant interaction effect between time and sex  as well as the principal effect of time . The linear model with covariates (M.2) revealed a non-statistically significant effect of the interaction between time and EXP-CVD19  or the two simple main effects . However, the model revealed a statistically significant linear interaction effect between time and sex  as well as the principal effect of time: both linear  and quadratic .The quadratic model with linear interaction (M.3) showed a non-statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex , EXP-CVD , and time  revealed a non-statistically significant effect. Only the main effect of time  became statistically significant.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear (2(1) = 4.726; p = 0.030]. In addition, the ΔAICc suggested a small difference M.3 and M.2 (2.63); the Wh of M.3 suggested that this model had 68% probability of being the best approximate model; and the Eh suggested that M.3 had a weight of evidence almost four times (3.72) greater than that of M.2 of being the best approximate model  revealed that the variance related to the random intercept of the participants was equal to 38.95.b = −2.474, SE = 3.768, t = −0.657, p = 0.513) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant interaction effect between time and sex . Only the main effect of time  became statistically significant.The linear model with covariates (M.2) revealed a non-statistically significant effect of the interaction between time and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant linear interaction effect between time and sex  as well as the principal effect of time: both linear  and quadratic .The quadratic model with linear interaction (M.3) showed a non-statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex , EXP-CVD , and time  revealed a non-statistically significant effect.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear  greater than M.3 of being the best approximate model (The comparison of the different multilevel growth curve models suggested that the linear model with covariates (M.2) showed the lower BIC and the lower AICc. The LRT showed that M.2 was statistically significantly different from M.1 (intercept model with covariates). However, despite that M.2 was not statistically significantly different from M.3, it was the most parsimonious, and thus, it was chosen as the best model. Indeed, the ΔAICc suggested a small difference M.2 and M.3 (1.66), the te model .Preliminary analyses (M.0) revealed that the variance related to the random intercept of the participants was equal to 6.25.b = −0.557, SE = 2.019, t = −0.276, p = 0.783) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19 , and of the simple main effect of EXP-CVD19 . However, the model revealed a non-statistically significant main effect of sex . Moreover, the model revealed a non-statistically significant interaction effect between time and sex  as well as the principal effect of time . The linear model with covariates (M.2) revealed a statistically significant effect of the interaction between time and EXP-CVD19 , and of the simple main effect of EXP-CVD19 . However, the model revealed a non-statistically significant main effect of sex . Moreover, the model revealed a non-statistically significant linear interaction effect between time and sex . The model showed a statistically significant effect of time as linear  but not as a quadratic term .The quadratic model with linear interaction (M.3) showed a statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex  and time  revealed a non-statistically significant effect. However, the model showed a statistically significant effect of EXP-CVD19 .Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear (Wh of M.3 suggested that this model had 54% probability of being the best approximate model (Wh of M.2 was 32%), and the Eh suggested that M.3 had a weight of evidence almost two times (1.7) greater than M.2 of being the best approximate model  showed the lower BIC, but the quadratic model (M.3) showed the lower AICc. The LRT showed that M.2 was not statistically significantly different from M.3. However, M.2 was the most parsimonious and thus was chosen as best model. Also the effect size indices suggested a negligible preference for M.3 instead of M.2. Indeed, the ΔAICc suggested a very small difference M.2 and M.3 (1.66), the te model .Preliminary analyses (M.0) revealed that the variance related to the random intercept of the participants was equal to 14.70.b = 1.333, SE = 2.518, t = 0.529, p = 0.598) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant interaction effect between time and sex  but only a statistically significant principal effect of time .The linear model with covariates (M.2) revealed a non-statistically significant effect of the interaction between time and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant linear interaction effect between time and sex  and the principal effect of time as a quadratic term . Conversely, the model showed a statistically significant principal effect of time as a linear term .The quadratic model with linear interaction (M.3) showed a non-statistically significant effect of the interaction between time and EXP-CVD19  or quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex , EXP-CVD19 , and the time both linear  and quadratic  revealed a non-statistically significant effect.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear (Wh of M.2 suggested that this model had 47% probability of being the best approximate model (Wh of M.3 was 46%), and the Eh suggested that M.2 had a weight of evidence of 1.01 greater than M.3 of being the best approximate model (The comparison of the different multilevel growth curve models suggested that the linear model with covariates (M.2) showed the lower BIC and the lower AICc. The LRT showed that M.2 was statistically significantly different from M.1 (intercept model with covariates). However, although M.2 was not statistically significantly different from M.3, it was the most parsimonious—and thus, it was chosen as the best model. However, the effect size indices suggested a negligible preference for M.2. Indeed, the ΔAICc suggested a very small difference M.2 and M.3 (0.02), the te model .Preliminary analyses (M.0) revealed that the variance related to the random intercept of the participants was equal to 5.53.b = −1.419, SE = 1.540, t = −0.922, p = 0.359) or the main effect of EXP-CVD19 . Instead, the model shows a significant effect of sex .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant interaction effect between time and sex  but a statistically significant principal effect of time .The linear model with covariates (M.2) revealed a non-statistically significant effect of the interaction between time and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant linear interaction effect between time and sex  as well as the principal effect of time: both linear  and quadratic .The quadratic model with linear interaction (M.3) showed a non-statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex , EXP-CVD19 , and time  revealed a non-statistically significant effect.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear  greater than M.3 of being the best approximate model (The comparison of the different multilevel growth curve models suggested that the linear model with covariates (M.2) showed the lower BIC and the lower AICc. The LRT showed that M.2 was statistically significantly different from M.1 (intercept model with covariates). However, despite that M.2 was not statistically significantly different from M.3, it was the most parsimonious—and thus, it was chosen as the best model. However, the effect size indices suggested a small preference for M.2. Indeed, the ΔAICc suggested a small difference M.2 and M.3 (1.78), the te model .Preliminary analyses (M.0) revealed that the variance related to the random intercept of the participants was equal to 2.81.b = 0.801, SE = 1.339, t = 0.598, p = 0.551) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant interaction effect between time and sex  as well as the principal effect of time .The linear model with covariates (M.2) revealed a non-statistically significant effect of the interaction between time and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant linear interaction effect between time and sex  as well as the principal effect of time: both linear  and quadratic .The quadratic model with linear interaction (M.3) showed a non-statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex , EXP-CVD19 , and time  revealed a non-statistically significant effect.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear (Wh of M.2 suggested that this model had 47% probability of being the best approximate model (Wh of M.0 was 23%), and the Eh suggested that M.2 had a weight of evidence two times (2.05) greater than M.0 of being the best approximate model  showed the lower BIC, but the linear model (M.2) showed the lower AICc. The LRT showed that M.0 was not statistically significantly different from M.1 (intercept model with covariates) but M.2 was statistically significantly different from M.1. In addition, the effect size indices suggested a negligible preference for M.2. Indeed, the ΔAICc suggested a very small difference M.2 and M.0 (1.44), the te model .Preliminary analyses (M.0) revealed that the variance related to the random intercept of the participants was equal to 16.37.b = 0.514, SE = 3.224, t = −0.159, p = 0.874) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  and the EXP-CVD19 simple main effect . Moreover, the model revealed a non-statistically significant interaction effect between time and sex  and the sex simple main effect . Only the main effect of time  became statistically significant.The linear model with covariates (M.2) revealed a statistically significant effect of the interaction between time and EXP-CVD19  and the simple main effects of EXP-CVD19 . However, the model revealed a non-statistically significant linear interaction effect between time and sex  and the sex simple main effect . The effects of time both linear  and quadratic  became statistically significant.The quadratic model with linear interaction (M.3) showed a statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex  and time  revealed a non-statistically significant effect. The main effects of EXP-CVD19  and the time as a linear term  became statistically significant.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear  greater than M.4 of being the best approximate model (The comparison of the different multilevel growth curve models suggested that the quadratic model with linear covariates interaction (M.3) showed the lower BIC and the lower AICc. The LRT showed that M.3 was statistically significantly different from M.2 (linear model with covariates). However, the LRT suggested that M.3 was not statistically significantly different from M.4, but it was more parsimonious—and thus, M.3 was chosen as the best model. However, the effect size indices suggested a small preference for M.3. Indeed, the ΔAICc suggested a small difference M.3 and M.4 (2.38), the te model .Preliminary analyses (M.0) revealed that the variance related to the random intercept of the participants was equal to 5.82.b = 0.013, SE = 2.269, t = 0.006, p = 0.996) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant interaction effect between time and sex . Only the main effect of time  became statistically significant.The linear model with covariates (M.2) revealed a non-statistically significant effect of the interaction between time and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant linear interaction effect between time and sex  as well as the principal effect of time as a quadratic term . Only the main effect of time as a linear term  became statistically significant.The quadratic model with linear interaction (M.3) showed a non-statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Furthermore, also the simple main effects of sex , EXP-CVD19 , and time  revealed a non-statistically significant effect.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear (Wh of M.3 suggested that this model had 47% probability of being the best approximate model (Wh of M.2 was 46%), and the Eh suggested that M.3 had a weight of evidence of 1.01 greater than M.2 of being the best approximate model  showed the lower BIC, but the quadratic model (M.3) showed the lower AICc. The LRT showed that M.3 was not statistically significantly different from M.2. However, despite that M.2 was not statistically significantly different from M.3, it was the most parsimonious—and thus, it was chosen as the best model. However, the effect size indices suggested a negligible preference for M.3. Indeed, the ΔAICc suggested a very small difference M.3 and M.2 (0.02), the te model .Preliminary analyses (M.0) revealed that the variance related to the random intercept of the participants was equal to 1.18.b = −0.248, SE = 0.792, t = −0.313, p = 0.755) or their main effects .The null model with covariates (M.1) revealed a non-statistically significant effect of the interaction between sex and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant interaction effect between time and sex . Only the main effect of time  became statistically significant.The linear model with covariates (M.2) revealed a non-statistically significant effect of the interaction between time and EXP-CVD19  or the two simple main effects . Moreover, the model revealed a non-statistically significant linear interaction effect between time and sex  as well as the principal effect of time: both linear  and quadratic .The quadratic model with linear interaction (M.3) showed a non-statistically significant effect of the interaction between time and EXP-CVD19  nor quadratic . In addition, M.4 showed also a non-statistically significant effect of the interaction between time and sex, neither linear  nor quadratic . Moreover, also the simple main effects of sex , EXP-CVD19 , and time  revealed a non-statistically significant effect.Finally, the quadratic model with all covariates interactions (M.4) showed a non-statistically significant effect of the interaction between time and EXP-CVD19, neither linear (Wh of M.2 indicates that this model had 61% probability of being the best approximate model (Wh of M.3 was 33%), and the Eh suggested that M.2 had a weight of evidence almost two times (1.87) greater than M.3 of being the best approximate model (The comparison of the different multilevel growth curve models suggested that the linear model with covariates (M.2) showed the lower BIC and the lower AICc. The LRT showed that M.2 was statistically significantly different from M.1 (null model with covariates). However, the LRT suggested M.2 was not statistically significantly different from M.3, but it was more parsimonious—and thus, M.3 was chosen as the best model. However, the effect size indices suggested a small preference for M.2. Indeed, the ΔAICc suggests a small difference M.2 and M.3 (1.25), the te model .As stated above, in addition to being a public physical health emergency, the COVID-19 pandemic also implies a global mental health emergency that may have a potential traumatic nature and provoke complex emotional responses that could negatively affect individual and collective mental health . TherefoInternalizing and Externalizing problems on 97 Italian young adults living in the Campania region, Southern Italy. A GCA . Moreover, despite the longitudinal panel, the study is an observational study. In this sense, experimental manipulations and a control group are lacking. Future researches need to extend the young adults’ mental health assessment to other Italian regions, taking into consideration that in the South of Italy, where the study was carried out, the COVID-19 outbreak has been taken moderately and was under control, compared with the North. Higher levels of distress might be hypothesized in places where very high numbers of losses and deaths have been registered. Moreover, the present study investigated the internalizing and externalizing problems as individual responses to COVID-19 pandemic; further investigations to measure the traumatic symptomatology and the characteristics of post-traumatic effects caused by such stressful events are needed . Follow-unctions . In thisunctions  as well unctions  post-locunctions . These aIn conclusion, the present study could contribute to the ongoing debate concerning the psychological impact of the COVID-19 emergency, helping to develop efficient and person-centered intervention projects able to take care of young adults’ mental health in the medium and long terms, understanding their specific needs and susceptibilities . This isanna.parola@un ina.it.The datasets generated in this article are not readily available because to ensure the privacy of the participants. Requests to access the datasets should be directed to AP, The studies involving human participants were reviewed and approved by the Ethical Committee of Psychological Research of University of Naples Federico II and was carried out in accordance with the American Psychological Association rules. The patients/participants provided their written informed consent to participate in this study.AP developed the theoretical framework of the present study, designed the study, and developed the methodological approach. AR performed all the analyses and designed tables and figures. FT and GT led the literature search and interpretation of data. SM critically revised the manuscript. All authors read and approved the final version of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This study investigated the effects of static stretching (SS) delivered with the same load but using two protocols – high-intensity and short-duration and low-intensity and long-duration – on range of motion (ROM) and muscle stiffness. A total of 18 healthy students participated in the study. They randomly performed high-intensity and short-duration (120% and 100 s) or low-intensity and long-duration (50% and 240 s) SS. Outcomes were assessed on ROM, passive torque at dorsiflexion ROM, and shear elastic modulus of the medial gastrocnemius before and after static stretching. The results showed that ROM increased significantly at post-stretching compared to that at pre-stretching in both high-intensity and short-duration [+6.1° ± 4.6° (Δ25.7 ± 19.9%)] and low-intensity and long-duration [+3.6° ± 2.3° (Δ16.0 ± 11.8%)]. Also, the ROM was significantly higher at post-stretching in high-intensity and short-duration conditions than that in low-intensity and long-duration. The passive torque at dorsiflexion ROM was significantly increased in both high-intensity and short-duration [+5.8 ± 12.8 Nm (Δ22.9 ± 40.5%)] and low-intensity and long-duration [+2.1 ± 3.4 Nm (Δ6.9 ± 10.8%)] conditions, but no significant differences were observed between both conditions. The shear elastic modulus was significantly decreased in both high-intensity and short-duration [−8.8 ± 6.1 kPa (Δ − 38.8 ± 14.5%)] and low-intensity and long-duration [−8.0 ± 12.8 kPa (Δ − 22.2 ± 33.8%)] conditions. Moreover, the relative change in shear elastic modulus in the high-intensity and short-duration SS was significantly greater than that in low-intensity and long-duration SS. Our results suggest that a higher intensity of the static stretching should be conducted to increase ROM and decrease muscle stiffness, even for a short time. Static stretching (SS) increases range of motion (ROM) and decreases muscle stiffness  and is cRecently, Research into the effects of resistance training concluded that total work, which was calculated from training intensity, repetitions, and number of sets, was important for muscle hypertrophy . We assuA randomized crossover design was used to investigate the effect of the two SS protocols on ROM and muscle stiffness of the dominant leg, which was determined by kicking a ball. The two SS conditions – high-intensity and short-duration stretching (120% intensity and 100 s) and low-intensity and long-duration stretching (50% intensity and 240 s) – were performed randomly with an interval of more than 1 week. Our choice of SS intensity and duration was based on previous research . DorsiflA total of 18 healthy young adults  who did not play sports or have a high activity daily participated in the study . In previous studies, the effect of SS has not been investigated separately for men and women . TherefoParticipants were seated in an isokinetic dynamometer  chair at 0° knee angle  and 70° hip flexion to prevent tension at the back of the knee, with adjustable belts over the trunk and pelvis . We adjuμ (kPa) = ρVs2, where ρ is muscle mass density . The average value of shear elastic modulus obtained from two ultrasound images was used for analysis.We used ultrasonic shear wave elastography  with a 5–14 MHz linear probe to measure the shear elastic modulus of MG. A previous study showed that the reliability of shear elastic modulus measurements used by the elastography device was very high  to analyze the interaction and main effect. Furthermore, we used the Bonferroni ed large . All staF = 7.0, p = 0.017, ηp2 = 0.291). The post hoc test revealed a significant increase in DF ROM after SS in the high-intensity and short-duration condition  and the low-intensity and long-duration condition . Moreover, DF ROM at POST in the high-intensity and short-duration protocol was significantly higher than that in the low-intensity and long-duration protocol . The relative change in DF ROM was significantly greater in the high-intensity and short-duration condition than the low-intensity and long-duration condition . Passive torque at DF ROM was significantly increased from PRE to POST in both the high-intensity and short-duration condition  and low-intensity and long-duration condition . However, the relative change in passive torque at DF ROM was no significant differences between conditions . Shear elastic modulus of MG was significantly decreased from PRE to POST in both the high-intensity and short-duration condition  and low-intensity and long-duration condition . Moreover, the relative change in shear elastic modulus of MG was significantly greater in the high-intensity and short-duration condition and the low-intensity and long-duration condition .All variables in both protocols are presented in We investigated the effects of high-intensity, short-duration and low-intensity, long-duration of SS with the same total work on DF ROM and shear elastic modulus of MG. Our results showed that DF ROM was increased significantly for both conditions, and DF ROM after the high-intensity and short-duration condition was significantly greater than that after the low-intensity and long-duration condition. Conversely, passive torque at DF ROM changed significantly after both protocols, with no significant difference between the two protocols. Shear elastic modulus was decreased significantly for both conditions, and shear elastic modulus after the high-intensity and short-duration condition was significantly greater than that after the low-intensity and long-duration condition. To the best of our knowledge, our study is the first to investigate the effects of high-intensity and short-duration and low-intensity and long-duration SS with the same impact of intensity and duration including 120% intensity on shear elastic modulus.Our results showed that DF ROM significantly increased after both high-intensity and short-duration SS and low-intensity and long-duration protocol. The previous studies showed that ROM significantly increased after 120% intensity or 50% intensity of SS – the intensities chosen for this study – and this result was similar to that of previous research . In addiρ = 0.68, data was not shown) than for the low-intensity and long-duration protocol . Thus, our results suggest that the stretching intensity could be more important for changes in DF ROM than stretching duration with the same load of intensity and duration.Interestingly, our study showed that DF ROM at POST in the high-intensity and short-duration protocol was higher than that in the low-intensity and long-duration protocol. This study showed that shear elastic modulus of MG was significantly decreased after both SS protocols. In this study, our results showed a significant increase in the DF ROM and a decrease in the shear elastic modulus of MG after both high-intensity and short-duration and low-intensity and long-duration SS. Therefore, in order to increase the ROM or decrease the shear elastic modulus, both stretching protocols might be prescribed. However, because high-intensity and short-duration stretching is more effective in changing the ROM and the shear modulus rather than low-intensity and long-duration stretching, it is effective to prescribe high-intensity and short-duration stretching if high-intensity stretching is possible. On the other hand, because high-intensity stretching is painful , if highThis study investigated the effect of high-intensity and short-duration or low-intensity and long-duration SS protocols with the same total work on DF ROM and shear elastic modulus. The results showed that DF ROM increased significantly in both protocols, and the increase was greater in the high-intensity and short-duration protocol than in low-intensity and long-duration. Conversely, passive torque at DF ROM (stretch tolerance) increased significantly after SS in both protocols, with no significant difference between the two protocols. On the other hand, shear elastic modulus was decreased significantly for both conditions, and shear elastic modulus after the high-intensity and short-duration condition was significantly greater than that after the low-intensity and long-duration condition. Our results suggest that a higher intensity of SS has greater effects on DF ROM and muscle stiffness even if stretching duration was shorter.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the ethics committee at the Niigata University of Health and Welfare, Niigata, Japan. The patients/participants provided their written informed consent to participate in this study.TF, RK, SS, KYh, KYs, RO, and MN: conceptualization, investigation and methodology. TF and MN: data curation. TF: formal analysis, visualization and writing – original draft. MN: funding acquisition, project administration, supervision, writing – review and editing. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined.The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP.In total, 60 healthy adults were recruited to participate in this study . A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis.Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES , ICC , and Spearman correlation coefficient values . Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF.Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices  to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement. Smartphone apps are recognized as convenient tools for our daily life activities in modern society. For health and fitness issues, there is an increasing number of smartphone users that utilize multiple free mobile phone apps to assess biosignals ,2, psychHRV is a physiological marker of cardiac autonomic responses that can be detected by recording heartbeat intervals over time. Assessment of daily HRV can provide useful information for understanding cardiac health with regards to labor force workload , mental Recently, ultra–short-term recordings for HRV assessment have received notable attention in cardiovascular medicine -15, metaToday, several HRV smartphone apps have been developed to evaluate autonomic health by using photoplethysmography ,22,23. HIn total, 60 healthy adults were recruited for this study . Inclusion criteria were healthy adults aged between 20 and 30 years. Exclusion criteria included current neurological, cardiovascular, and metabolic diseases. All participants signed an informed consent form and were familiarized with experimental procedures. The participants were requested to avoid vigorous exercise 24 hours before visits and to avoid caffeine-containing substances and smoking 2 hours before the experiments. This study was approved by the Human Ethics Committee of University of Taipei (IRB-2019-005) and was conducted according to the Declaration of Helsinki and its later amendments.t tests carried out in G*Power  = VLF (ms2) / total power (ms2) × 100 (2)nLF[nu] = LF (ms2) / total power (ms2) × 100 (3)nHF[nu] = HF , small (0.2-0.6), moderate (0.6-1.2), large (1.2-2.0), and very large (>2.0) [r). The level of the correlation coefficients was determined as trivial (r<0.1), small (0.1<r<0.3), moderate (0.3<r<0.5), high (0.5<r<0.7), very high (0.7<r<0.9), nearly perfect (r>0.9), and perfect (r=1) [Statistical analyses were conducted using SPSS Statistics  and Microsoft Excel 2013 (Microsoft Corp). Descriptive data of the measured variables are presented as median and interquartile range (25%-75%). Magnitude of difference and agreement of HRV indices in all time segments (with the 5-minute criterion as a reference) were analyzed by using the standardized differences of variables (effect size: ES), Cohen e (>2.0) . In terme (>2.0) . The corct (r=1) . Lastly,ct (r=1) .The descriptive information and standardized differences of HRV indices for all time segments of the ECG and PEP measurements are presented in In The results demonstrated similar outcomes for ICC values for the ECG and PEP measurements. The SDNN, RMSSD, and pNN50 ICC values were nearly perfect in all ultra–short-term and short-term records compared to the 0-5–minute standard ECG measurement . Furthermore, the time-domain variables of PEP were very large to nearly perfect for ultra–short-term recordings, except the 0-1–minute time segment (0.81-0.94). In terms of frequency-domain analysis, nearly perfect ICC values were found in the 0-4–minute time segment of the nVLF, nLF, and nHF (0.92-0.96). Very large ICC values were found in the 0-3–minute time segments for nLF and nHF (0.80-0.82). A broad range of ICC values was identified among the other comparisons . Furthermore, the correlation coefficients were very large for the time-domain variables for ultra–short-term recordings using PEP (0.80-1.0), except for nearly perfect values for the 0-1–minute time segment. For frequency-domain analysis, a nearly perfect correlation coefficient was only found for 0-4–minute recordings (0.91-0.96). Furthermore, a very large correlation coefficient was found in the nLF and nHF 0-3–minute recordings (0.77-0.81). In contrast, a wide range of values was identified among the other comparisons . Lastly, for the frequency-domain analysis (nLF and nHF), a recording of at least 3 minutes is required for accurate and valid PEP HRV assessment.Based on our observations, a 1-minute ultra–short-term HRV recording for the time-domain analysis revealed valid and reliable HRV features (with the 5-minute criterion as reference), despite an initial 5-minute stabilization. This indicates that the PEP app is a convenient surrogate for taking HRV measurements. It is suggested that the RMSSD is independent of respiratory sinus arrhythmia and is associated with high-frequency changes of HR modulation in response to respiratory patterns due to its strength of mathematical calculation . The RMSIt is arguable that the PEP presented valid and reliable measurements in SDNN accompanied by RMSSD and pNN50 for any HRV epoch. It seems that SDNN and pNN50 are good options to integrate time-domain HRV indices. However, as the accuracy of ultra–short-term measurements of SDNN may be influenced by psychological conditions  ,13, usinIt is important to note that nVLF, nLF, and nHF showed trivial or small differences in association with a large variation in ICC values, correlation coefficients, and bias across all time segments compared to the standard 0-5–minute criterion. The poor validity and reliability of nVLF, nLF, and nHF in shortened epochs could be related to interindividual variations in breathing rates during measurements. Interindividual variations in breathing patterns could increase the risk of increasing HR oscillations in different time segments. Respiratory rhythm is thought of as an essential way to record frequency-domain variables such as LF and HF due to oscillations in HR responses . HoweverOur findings suggest using consecutive HRV recordings of at least 3 minutes when the PEP app is used to monitor frequency-domain variables. In contrast, the minimum time requirement for HF and LF recordings has been suggested as 1 and 2 minutes, respectively ,40. CastIn an attempt to identify the agreement of biosignal measurements between the ECG and PEP, a Bland-Altman analysis was performed to compare the limit of agreement of ultra–short-term and short-term HRV recordings of the SDNN, RMSSD, pNN50, nVLF, nLF, and nHF. It is interesting to note that the PEP HRV recordings showed similar outcomes for the SDNN, RMSSD, pNN50, nVLF, nLF, and nHF measurements for all time segments, as compared to conventional lead II ECG recordings. This study revealed the accuracy and acceptance of PEP HRV recordings after consecutive 1-minute recordings in the time-domain analysis. In contrast, the degree of agreement between the ECG and PEP was relatively low for the first 3-minute assessment when frequency-domain analysis was computed. One possible explanation for less accurate measurements of frequency-domain HRV variables with shorter duration recordings may be the lack of a detrending method for processing spectral signals in the PEP app . AnotherThe first limitation of this study is that a telemetric HR sensor and a chest strap were required to detect HR responses during the PEP measurement, and that these accessories may not be commonly owned by the general population. In addition, the recording position and the HR chest strap might not be comfortable for specific populations  and clinical settings. Despite the abovementioned limitations, this is a novel study that reports the validity and accuracy of the PEP app for short-term HRV recordings.Time management is critical for professionals, including clinical practitioners and strength and conditioning coaches of elite sports teams. The PEP app is compatible with the Android operating system and can be used on low-cost smartphones. As growing numbers of studies focus on the methodological issues related to utilizing ultra–short-term HRV recordings, the number of nonprofessionals using this free mobile app can easily be increased. We suggest that future studies should examine the use of PEP HRV assessments in the context of multidisciplinary approaches .The accuracy and reliability of the LF and HF measurements are critical to interpreting the shift of sympathovagal activities ,45. ExceIn conclusion, the PEP smartphone app provides reliable and valid HRV data. It is appropriate to use the PEP app to facilitate 1-minute ultra–short-term HRV recordings during stabilization to save time when the time-domain analysis is used. Caution should be taken when the frequency-domain analysis is implemented for the interpretation of cardiac autonomic modulation. Consecutive recordings of at least 3 minutes during stabilization are suggested for accurate measurement of frequency-domain nLF and nHF indices. The use of the PEP smartphone app for ultra–short-term and short-term HRV recordings is recommended as an easy and user-friendly tool to monitor cardiac autonomic health in people with various lifestyles.